EE225B, Spring 2018
Digital Image Processing
Tue. and Thu.: 09:30  11:00 am
540 Cory
Prerequisite: EE120
Required Text:

R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 4th Edition.
Video lectures:
EE225B, Spring 2018
Course Details:

Lecturer:
Professor Avideh Zakhor
avz@eecs.berkeley.edu
507 Cory Hall
Phone: (510) 6436777
Office Hours:
Thursday 11:00am  12:00pm in 507 Cory
TA:
Xinlei Pan
xinleipan@berkeley.edu
Office Hours:
Monday and Friday, 45 pm, location: Cory 504

Recommended Texts:

Bovik, Handbook of Image and Video Processing, Academic Press 2000.

N. Netravali and Barry G. Haskell, Digital Pictures, Plenum Press, 1988.
 W.K.Pratt, Digital Image Processing, John Wiley and Sons, 1992.
 A.M. Tekalp, Digital Video Processing, Prentice Hall, 1995.
Other useful references:

D. E. Dudgeon and R. M. Mersereau, MultiDimensional Digital Signal Processing, Prentice Hall, 1984.

V. Oppenheim and R. W. Schafer, Digital Signal Processing, PrenticeHall, 1975.

T. S. Huang, editor, TwoDimensional Digital Signal Processing, Topics in Applied Physics, vol. 42 and vol. 43, SpringerVerlag, 1981.

S. K. Mitra and M. P. Ekstrom, editors, TwoDimensional Digital Signal Processing, Dowden, Hutchison, and Ross, 1978.

R. C. Gonzalez and P. Wintz, Digital Image Processing, AddisonWesley, 1979.

H. C. Andrews and B. R. Hunt, Digital Image Restoration, PrenticeHall, 1977.

H. C. Andrews, Tutorial and Selected Papers in Digital Image Processing, IEEE Press, 1978.

W. F. Schrieber, Fundamentals of Electronic Imaging Systems, SpringerVerlag, 1986.

K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989.
Outline of Topics:

Image sensing and acquisition, sampling, quantization

Spatial transformations, filtering in space domain and frequency domain.

Image restoration, enhancement, reconstruction; computed tomography

Wavelets and multiresolution processing

Image and video compression and communication; watermarking

Morphological Image processing

Color processing

Edge detection; feature extraction; SIFT, MSER

Image segmentation

Neural networks and deep learning

3D image processing

Applications to augmented reality and virtual reality
Homework:
Homework will be issued approximately once every one or two weeks. They will either consist of written assignments, Matlab assignments or C programming assignments. Homework will be graded, and will contribute 55% to the final grade. Homework handed in late will not be accepted unless consent is obtained from the teaching staff prior to the due date. There will be a project that will constitute 35% of your grade. The project can be individual or in a group. You are to submit a proposal to the instructor by the end of March. More details on the project will be provided later, and a list of suggested topics will be provided. In addition, 10% of your grade will be for in class participation.
